Methods and devices for accurately classifying cardiac activity

ABSTRACT

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In illustrative examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. New methods for organizing the use of morphology and rate analysis in an overall architecture for rhythm classification and cardiac signal analysis are also discussed.

RELATED APPLICATIONS

The present application is a divisional U.S. patent application Ser. No.13/607,168, filed Sep. 7, 2012, which is a divisional U.S. patentapplication Ser. No. 12/637,438, filed Dec. 14, 2009 and published as USPatent Application Publication Number 2010-0094369, now U.S. Pat. No.8,265,749, which is a continuation of U.S. patent application Ser. No.12/399,914, filed Mar. 6, 2009, now U.S. Pat. No. 8,160,686 and titledMETHODS AND DEVICES FOR ACCURATELY CLASSIFYING CARDIAC ACTIVITY; whichclaims the benefit of and priority to U.S. Provisional PatentApplication Nos. 61/051,332, filed May 7, 2008 and titled METHODS ANDDEVICES FOR IDENTIFYING AND CORRECTING OVERDETECTION OF CARDIAC EVENTSand 61/034,938, filed Mar. 7, 2008 and titled ACCURATE CARDIAC EVENTDETECTION IN AN IMPLANTABLE CARDIAC STIMULUS DEVICE. Priority to and thebenefit of each of the aforementioned applications/patents is herebyclaimed again for the present application, and the entire disclosures ofeach of said applications/patents are incorporated herein by reference.

The present application is related to U.S. application Ser. No.12/399,901, filed Mar. 6, 2009, published as US Patent ApplicationPublication Number 2009-0228057 and titled ACCURATE CARDIAC EVENTDETECTION IN AN IMPLANTABLE CARDIAC STIMULUS DEVICE; which claims thebenefit of and priority to U.S. Provisional Patent Application No.61/034,938, filed Mar. 7, 2008, and the disclosures of which are alsoincorporated in their entirety herein by reference. The presentapplication is also related to U.S. patent application Ser. No.13/436,398, filed Mar. 30, 2012 and published as US Patent ApplicationPublication Number 2012-0197147, which is a divisional of U.S. patentapplication Ser. No. 12/399,914 claiming the benefit of and priority toU.S. 61/051,332 filed May 7, 2008 and U.S. 61/034,938 filed Mar. 7,2008, the disclosure of which is incorporated herein by reference.

FIELD

The present invention relates generally to implantable medical devicesystems that sense and analyze cardiac signals. More particularly, thepresent invention relates to implantable medical devices that capturecardiac signals within an implantee's body in order to classify cardiacactivity as likely benign or malignant.

BACKGROUND

Implantable cardiac devices typically sense cardiac electrical signalsin an implantee and classify the implantee's cardiac rhythm asnormal/benign or malignant. Illustrative malignant rhythms may includeventricular fibrillation and/or ventricular tachyarrhythmia. Theaccuracy with which an implantable medical device analyzes capturedsignals determines how well it makes therapy and other decisions.

New and/or alternative methods and devices for cardiac signal analysisare desired.

SUMMARY

Various illustrative embodiments of the present invention are directedtoward improved accuracy in cardiac signal analysis by implantablemedical devices. Some illustrative embodiments identify overdetection ofcardiac events. Some illustrative embodiments also correct at least somecaptured data and use the corrected data to make operational decisions.The invention may be embodied in methods and/or devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram for an illustrative method of identifyingoverdetection and taking corrective action;

FIGS. 2 and 24 are a block diagrams further illustrating examples ofidentifying overdetection and making therapy decisions;

FIG. 3 shows an illustrative implantable medical device;

FIG. 4 is an illustration of a detection profile that may be used whiledetecting cardiac events in an implantable medical device;

FIG. 5 is a graphical illustration of double detection where both R andT waves are detected in each cardiac cycle;

FIGS. 6A-6B show an illustrative method of morphological analysis of thedetections in FIG. 5, relative to a stored R-wave template;

FIGS. 7A-7B provide a detailed example of illustrative identification ofoverdetections using morphology analysis;

FIG. 8 shows an illustrative example of analysis to mark similar anddissimilar events in FIGS. 7A-7B;

FIG. 9 shows an illustrative oversensed cardiac signal havingalternating long-short-long intervals;

FIG. 10 illustrates analysis of an alternating interval overdetectionidentification method;

FIG. 11 shows an illustrative oversensed wide QRS complex;

FIGS. 12A-12D show illustrative application of wide-complexoverdetection identification rules;

FIGS. 13A-13B illustrate handling of outcomes from the rule set analysisof FIGS. 12A-12D;

FIG. 14 is a process flow diagram for an illustrative wide complexoverdetection identification method;

FIG. 15 provides a graphical illustration of data analysis fromdetection-to-detection for illustrative True-False marking;

FIG. 16 shows an illustrative example for integration of a waveformappraisal method with morphology, alternating interval and wide complexoverdetection methods;

FIG. 17 illustrates how modifications to the detection profile may failto avoid overdetection in some circumstances;

FIGS. 18-21 provide graphical illustrations of handling of suspect andoverdetection markers in a stream of captured events;

FIG. 22 is a process flow diagram for an illustrative chargeconfirmation method; and

FIG. 23 shows an illustrative method of analysis.

DETAILED DESCRIPTION

The following detailed description should be read with reference to thedrawings. The drawings, which are not necessarily to scale, depictillustrative embodiments and are not intended to limit the scope of theinvention.

Some of the following examples and explanations include references toissued patents and pending patent applications. These references are forillustrative purposes and are not intended to limit the presentinvention to the particular methods or structures from those referencedpatents and patent applications.

Unless implicitly required or explicitly stated, the methods below donot require any particular order of steps. It should be understood thatwhen the following examples refer to a “current event,” in someembodiments, this means the most recently detected cardiac event isbeing analyzed. However, this need not be the case, and some embodimentsperform analysis that is delayed by one or more detections and or afixed period of time.

The illustrative examples below use rectified captured signals forpurposes of event detection, for example, as shown in FIGS. 5, 7A (at148), 9, 11, 12C-12D, 17 and 18. Some illustrative examples performanalysis of shape characteristics (morphology) of the captured signalsusing an unrectified signal, as shown, for example, by FIGS. 6A-6B, 7A,11 and 12A-12D. Choices shown regarding use of rectified/unrectifiedsignals are merely illustrative, and may be changed if desired.

The nomenclature used herein indicates that a signal is sensed by animplantable cardiac device system, events are detected in the sensedsignal, and cardiac activity is classified by use of the detected events(detections). Rhythm classification includes the identification ofmalignant rhythms, such as ventricular fibrillation or certaintachyarrhythmias, for example. Implantable therapy systems maketherapy/stimulus decisions in reliance upon the classification of thecardiac rhythm.

In an illustrative example, a detected event is detected by comparingreceived signals to a detection threshold, which is defined by adetection profile. FIGS. 4 and 17, below, provide illustrative examplesof detection profiles. Some embodiments of the present inventionincorporate detection profiles and associated analysis as discussed inU.S. Provisional Patent Application No. 61/034,938, entitled ACCURATECARDIAC EVENT DETECTION IN AN IMPLANTABLE CARDIAC STIMULUS DEVICE, filedon Mar. 7, 2008. Any suitable detection profile may be used.

Detected events are separated by intervals, for example, as shown inFIG. 18 at 602. Several intervals can be used to generate an averageinterval across a selected number of intervals. Some examples shownbelow use four intervals to calculate an average interval. Some othernumber of intervals may be used, as desired. The detected heart rate canthen be calculated using the average interval.

A cardiac electrogram includes several portions (often referenced as“waves”) that, according to well known convention, are labeled withletters including P, Q, R, S, and T, each of which corresponds toparticular physiological events. It is typical to design detectionalgorithms to sense the R-wave, though any portion, if repeatedlydetected, can be used to generate a beat rate. If morphology (shape)analysis is used in addition to heart rate, the system may captureand/or analyze the portion of the cycle that includes the Q, R and Swaves, referred to as the QRS complex. Other portions of the patient'scardiac cycle, such as the P-wave and T-wave, are often treated asartifacts that are not sought for the purpose of estimating heart rate,though this need not be the case.

Typically, for purposes of ascertaining rate each cardiac cycle iscounted only once. Overdetection (such as a double or triple detection)may occur if the device declares more than one detected event within asingle cardiac cycle. FIGS. 5, 7A, 9, 11, 12C-12D and 17 each show, inone form or another, overdetection. Examples include the detection ofboth an R-wave and a trailing T-wave (see FIGS. 5, 7A, 9 and 17) as wellas multiple detections of a wide QRS complex (see FIGS. 11, 12C-12D and17). These examples are not intended to be exhaustive, and those skilledin the art understand that detection methods in implanted devices can bechallenged by any number of variations of “normal” cardiac activity. Forexample, a P-wave may be detected and followed by detection of atrailing part of the QRS or a T-wave from the same cardiac cycle.Overdetection may also occur if noise causes an event to be declaredwhen no cardiac event has taken place, for example, due to externaltherapy or noise, pacing artifact, skeletal muscle noise,electro-therapy, etc.

Overdetection can lead to overcounting of cardiac cycles. For example,if one cardiac cycle takes place and a detection algorithm declaresmultiple detected events, overdetection has occurred. If the heart rateis then calculated by counting each of these detections, overcountingoccurs. Calculated heart rates may be used alone or in combination withother factors to classify cardiac rhythms as malignant or benign.Overcounting in reliance on overdetected events can result inerroneously high rate calculation. Miscalculation of heart rate can leadto incorrect rhythm classification and therapy decisions. Someembodiments are directed to identifying overdetection and/or correctingaffiliated data.

FIG. 1 is a process flow diagram for an illustrative method ofidentifying overdetection and taking corrective action. The illustrativemethod begins with event detection 10, where the received cardiac signalis captured and compared to a detection threshold until the receivedsignal crosses the detection threshold, resulting in declaration of adetected event. FIGS. 4-5 provide illustration of detection step 10. Anadditional detection profile example is shown in FIG. 17 as well.

Next, the method performs an overdetection identification step 12. Thismay include one or more of several analysis methods including, asillustratively shown, morphology analysis 14, interval analysis 16 andwide QRS analysis 18. FIGS. 6A-6B, 7A-7B and 8 show illustrativemorphology analysis 14 as part of overdetection identification 12. FIGS.9-10 show illustrative interval analysis 16 as part of overdetectionidentification 12. FIGS. 11, 12A-12D, 13A-13B, and 14-15 showillustrative wide QRS analysis 18 as part of overdetectionidentification 12. FIG. 16 shows an example in which calculated beatrate is used to select from several overdetection identification methods14, 16, 18.

Following overdetection identification 12, if one or more overdetectionsare identified, the method corrects data, as shown at 20. FIGS. 18-21show illustrative data correction methods that can be performed in step20. If no data correction is needed at step 20, the method may simply goto the next step.

Finally, the method includes a therapy decision, as shown at 22. Atherapy decision 22 may classify a cardiac rhythm of the implantee. Thetherapy decision 22 may incorporate additional methods such as chargeconfirmation shown in FIG. 22. The method then iterates to eventdetection 10, as indicated by line 24.

The therapy decision 22 may include one or more of several forms ofanalysis. In one illustrative example, individual detected events aremarked as shockable or non-shockable and an X-out-of-Y counter ismaintained to determine whether the overall cardiac rhythm meritstherapy. The marking of individual events as shockable or non-shockablemay take several forms, including rate-based and/or morphology baseddeterminations, or combinations thereof Some illustrative factors andcombinations of factors that may be considered are discussed in U.S.Pat. No. 6,754,528, entitled APPARATUS AND METHOD OF ARRHYTHMIADETECTION IN A SUBCUTANEOUS IMPLANTABLE CARDIOVERTER/DEFIBRILLATOR, andU.S. Pat. No. 7,330,757 entitled METHOD FOR DISCRIMINATING BETWEENVENTRICULAR AND SUPRAVENTRICULAR ARRHYTHMIAS.

Therapy decision 22 may also take into account the persistence of amalignant condition. Some illustrative examples are shown in US PatentApplication Publication Number 2006-0167503, now U.S. Pat. No. 8,160,697and titled METHOD FOR ADAPTING CHARGE INITIATION FOR AN IMPLANTABLECARDIOVERTER-DEFIBRILLATOR. Other methods may be used as a part of thetherapy decision 22. A detailed example using multiple rate zones foridentifying shockable events in the therapy decision 22 is furtherdiscussed below.

The method of FIG. 1 includes overdetection identification 12 and datacorrection 20. These steps are designed to improve classificationoutcomes. The examples below provide details for implementing thesesteps in some illustrative embodiments.

FIG. 2 is a process flow diagram further illustrating an example ofidentifying overdetection and making therapy decisions. The method 30provides an example which incorporates each of several differentoverdetection identification steps, as well as additional analysis ofcaptured data for waveform appraisal. The illustrative method beginswith the declaration of a new detected event, as shown at 32 (againreference is made to FIGS. 4-5 and/or 17 to show detection thresholdusage in step 32).

The detected event undergoes waveform appraisal as indicated at 34.Waveform appraisal 34 analyzes data captured in association with thedetected event to ensure the detection is cardiac in origin. Waveformappraisal can mark detected events having significant noise as suspectevents. For example, noise may be identified by counting the number ofzero crossings of the signal, or of the first or second derivative ofthe signal, during a predetermined time period. U.S. Pat. No. 7,248,921,titled METHOD AND DEVICES FOR PERFORMING CARDIAC WAVEFORM APPRAISALprovides additional detailed examples of waveform appraisal 34.

If the detected event fails waveform appraisal 34, it is marked as asuspect event and the method returns to step 32 and awaits a nextdetection threshold crossing. Once a detected event is captured thatpasses waveform appraisal 34, the method 30 goes into steps foranalyzing detections and identifying overdetection. As shown at 36, theillustrative method 30 determines whether a morphology template exists.A morphology template is a data set useful for morphological comparisonwith recently detected event(s). Morphology templates may be formed byimplanted device systems or associated programmers, or may be selectedor identified by medical personnel. U.S. Pat. No. 7,376,458, entitledMETHOD FOR DEFINING SIGNAL TEMPLATES IN IMPLANTABLE CARDIAC DEVICESdiscusses some examples of template formation and/or testing. In someexamples, template formation is performed by identifying arepresentative QRS complex that is reflective of an average or typicalmorphology of a cardiac cycle for an implantee.

In an illustrative example of automatic template formation, a detectedevent is identified and data for the detected event is stored by adevice as a preliminary template. In the illustrative example, thepreliminary template can be validated by comparing the stored data todata captured for a number of adjacent-in-time detected events. If theset of adjacent-in-time detected events demonstrates high correlation toone another, the preliminary template is validated and a morphologytemplate is defined using the preliminary template. If the preliminarytemplate cannot be validated, it is discarded. Template formation mayfail if the captured signal persistently varies, since high variabilitymay prevent validation of a preliminary template. The query at step 36determines whether a template is available for use in morphologyoverdetection identification 38.

In some systems, a morphology template will always exist. For example,some embodiments allow a physician to select a representative beatduring implantation or during a telemetry session as a morphologytemplate, or a representative template may be selected from a library ofknown templates. If so, step 36 may be omitted.

At step 38, the morphology of one or more detected events is analyzed todetermine whether one or more detected events is likely the result ofoverdetection. Steps as shown below with reference to FIGS. 6A-6B, 7A-7Band 8 may be performed as part of step 38. This may include identifyingalternating patterns of morphology indicating High-Low-High correlationsto the morphology template.

Following step 38 (if a stored morphology template exists) or step 36(if there is no stored morphology template), the method continues at 40,where the measured heart rate of the implantee is considered. If therate falls into an AI Range (short for Alternating Interval Range) theillustrative example proceeds with Alternating Interval OverdetectionIdentification as shown at 42. In Alternating Interval OverdetectionIdentification 42, the intervals between detected events are analyzed todetermine whether overdetection is occurring. The Alternating IntervalOverdetection Identification 42 method may include steps as shown belowwith reference to FIGS. 9-10.

Returning to step 40, if the implantee heart rate falls into the WCRange (Wide QRS Complex range), then Wide Complex OverdetectionIdentification methods are called, as indicated at 44. Wide ComplexOverdetection Identification 44 is designed to identify overdetection ofwide QRS complexes, and may include the methods discussed below withreference to FIGS. 11, 12A-12D, 13A-13B and 14-15.

The AI Range and WC Range may be separate from one another, or there maybe overlap of these ranges such that each of steps 42 and 44 areperformed. Further discussion of the integration of these methods is setout with reference to FIG. 16, below. In yet another embodiment, each ofsteps 42, 44 are performed regardless of the calculated heart rate.

In FIG. 2, following the applicable overdetection identification steps38, 42 and/or 44, data correction may be invoked, as shown at 46. Datacorrection 46 is invoked when one or more of the overdetectionidentification steps 38, 42 and/or 44 identifies overdetection. If nooverdetection is identified, data correction 46 may be bypassed.

In some examples, data correction includes recalculation of intervalsbetween detected events by removing one or more identifiedoverdetections from analysis. For example, if an overdetection isidentified, then step 46 can manipulate stored data to correct for theoverdetection and reduce the calculated heart rate. FIGS. 18-21 furtherillustrate this concept in a particular series of examples.

The examples of FIGS. 18-21 buffer rate calculations from ongoingdetections by waiting until an interval between two detections is“certified” before using the interval for rate calculation. In someexamples, an interval is considered certified if it passes waveformappraisal 34 and the several overdetection identification steps 38, 42,44 without being marked as noise or as an overdetected event.

Following data correction 46, the method makes a therapy decision 48. Ifno therapy is needed, the method returns to block 32. If therapy isindicated at step 48, then charging and therapy delivery steps can beperformed, as shown at 50. Typically, implanted therapy devices usecharging circuitry that takes a period of time to prepare the device fortherapy delivery. The method may iterate several times after a charge isinitiated before therapy can be delivered. The specifics of steps 48 and50 may vary. Once therapy is indicated at 48, a system may ensure thattherapy continues to be indicated until it is delivered. US PatentApplication Publication Number 2006-0167503, now U.S. Pat. No. 8,160,697and titled METHOD FOR ADAPTING CHARGE INITIATION FOR AN IMPLANTABLECARDIOVERTER-DEFIBRILLATOR provides some illustrative examples of theseconcepts.

FIG. 24 is a block flow diagram similar to FIG. 2. FIG. 24 shows amethod in which, as mentioned above, both the alternating interval andwide-complex analysis are performed without rate limiting. The method isgenerally shown at 1000, and includes identifying a new detection 1002.Once an event is detected at 1002, analysis passes to waveform appraisal1004. If waveform appraisal 1004 fails, the detected event is marked assuspect and the method returns to waiting for a new detection 1002.

If waveform appraisal 1004 is passed, the method continues to determinewhether a morphology template exists, as shown at 1006. If so, themethod continues to Morphology Overdetection Identification 1008. Asnoted above, in some embodiments block 1006 may be skipped, as forexample if a morphology template always exists for the system.

In Morphology Overdetection Identification 1008, template comparisons ofdetected events are analyzed to determine whether overdetection appearslikely. If so, the method continues to data correction 1010. Otherwise,the method continues to Alternating Interval OverdetectionIdentification 1012, in which intervals between detected events areanalyzed to determine whether overdetection appears likely. Again, ifso, the method continues to data correction 1010. Otherwise, the methodcontinues to Wide Complex Overdetection Identification 1014, in whichpairs of events are analyzed using interval and morphology informationto identify likely overdetections. If overdetection appears likely inWide Complex Overdetection Identification 1014, the method again goes todata correction 1010. Otherwise, the method continues to making atherapy decision 1016.

If Data Correction 1010 is called from any of blocks 1008, 1012 and/or1014, then one or more overdetected events has been identified, andassociated data is corrected. This may include combining intervalsaround the identified overdetection to create a single, longer intervalwhere two relatively shorter intervals were once identified. Examplesare shown, for example, in FIGS. 20-21.

If no therapy is needed at block 1016, the method returns to the newdetection block 1002. If therapy is indicated, the method continues toCharging and Therapy Delivery block 1018. As noted above, the Chargingand Therapy delivery block 1018 may be called multiple times beforetherapy is delivered, as relevant subsystems prepare for therapy, forexample while waiting for a therapy delivery capacitor to be charged.

FIG. 3 shows an illustrative implantable medical device and implantlocation. More particularly, an illustrative subcutaneous-only system isshown in FIG. 3. The subcutaneous system is shown relative to a heart60, and includes a canister 62 coupled to a lead 66. The canister 62preferably houses operational circuitry for performing analysis ofcardiac activity and for providing a therapy output. The operationalcircuitry may include batteries, input/output circuitry, powercapacitors, a controller, memory, telemetry components, etc., as knownin the art.

Electrodes are disposed at locations throughout the system including,for example, an electrode 64 on the canister 62, and electrodes 68, 70,72 on lead 66. The electrodes 64, 68, 70, 72 may take any suitable formand can be made of any suitable material. For example, the canisterelectrode 64 may be an isolated button electrode or it may be a regionor surface of the canister 62, and the electrodes 68, 70, 72 on lead 66may be coil electrodes, ring electrodes, or other structures known inthe art.

The electrodes 64, 68, 70, 72 define a plurality of sensing vectors suchas V1, V2, V3 and, optionally, V4. If desired, one or more vectors V1,V2, V3, and V4 may be chosen as a default sensing vector, for example,as discussed in US Patent Application Publication Number 2007-0276445titled SYSTEMS AND METHODS FOR SENSING VECTOR SELECTION IN ANIMPLANTABLE MEDICAL DEVICE. Other uses of multiple vectors are shown,for example, in U.S. Pat. No. 7,392,085 titled MULTIPLE ELECTRODEVECTORS FOR IMPLANTABLE CARDIAC TREATMENT DEVICES. Another embodimentconsiders posture in vector analysis, for example, as discussed in USPatent Application Publication Number 2008-0188901, now U.S. Pat. No.8,200,341 and titled SENSING VECTOR SELECTION IN A CARDIAC STIMULUSDEVICE WITH POSTURAL ASSESSMENT. Multiple sensing vectors may beanalyzed, sequentially or in combination, as desired.

Therapy may be applied using any chosen pair of electrodes. Anillustrative example uses the can electrode 64 and the coil electrode 72to apply therapy. Other electrode combinations may be used. Therapy mayinclude mono-, bi- or other multi-phasic defibrillation and/or variouspacing operations.

The present invention is not limited to any particular hardware, implantlocation or configuration. Instead, it is intended as an improvementupon any implantable cardiac system. Some illustrative examples canassociate with an external programmer 74 configured to communicate withthe implanted device for various purposes, including, for example andwithout limitation, one or more of the following: device testing; uploadnew/revised software; modify sensing, detection or therapy settings;determine the status of device operation, battery life, or leadintegrity; and/or download data relating to the implantee's condition,prior data capture, or treatment. Any suitable communication method maybe used, such as various protocols and hardware widely known in the art.

FIG. 3 omits several anatomical landmarks. The illustrative system shownmay be implanted beneath the skin outside of the ribcage of theimplantee. The location illustratively shown would place the canister 62at approximately the left axilla of the implantee, level with thecardiac apex, with the lead 66 extending medially toward the xiphoid andthen toward the head of the implantee along the left side of thesternum. One illustrative example uses a method/system as shown incommonly assigned US Patent Application Publication Number 2006-0122676,now U.S. Pat. No. 7,655,014 and titled APPARATUS AND METHOD FORSUBCUTANEOUS ELECTRODE INSERTION. Other illustrative subcutaneoussystems and locations are shown in commonly assigned U.S. Pat. Nos.6,647,292, 6,721,597 and 7,149,575.

The present invention may also be embodied in systems having variousimplant configurations including, for example, other subcutaneous-only,vascular-only, and/or transvenous implantation configurations/locations.The canister 62 may be placed in anterior, lateral, and/or posteriorpositions including, without limitation, axillary, pectoral, andsub-pectoral positions, as well as placements on either the left orright side of the implantee's torso and/or in the abdomen. Entirelyintravascular implantation of the system has also been proposed. Thelead 66 may be placed in any of a number of suitable configurationsincluding anterior-posterior combinations, anterior-only combinations,transvenous placement, or other vascular placements.

FIGS. 4-5 illustrate a detection profile and how its use, in givencircumstances, may lead to overdetection. Referring to FIG. 4, adetection profile is shown at 80 as including a refractory period whichis followed by an exponential decay. For illustrative purposes, theheight of the refractory period is shown as the “Estimated Peak.” TheEstimated Peak is an implantable systems' estimate of the peak amplitudeof captured cardiac signals. The use of Estimated Peak allows thedetection profile to adapt to the amplitude of captured signals.

The decay slope of detection profile 80 uses the Estimated Peak (or, insome embodiments, a percentage of the Estimated Peak) as its startingpoint. The decay approaches the sensing floor over time. The sensingfloor may be the ultimate floor or highest sensitivity of the system, orit may be set to a predetermined level. Multiple decays may be used, asshown in U.S. Provisional Patent Application No. 61/034,938. The decaymay be exponential or may take some other shape such as a straight-linedecay, stepped function, etc.

FIG. 5 shows application of the detection profile 80 from FIG. 4 to acaptured signal, which is shown at 104. Refractory periods are shown incross-hatching at 100, 106, 112, and 118. Exponential decays 102, 108,114 follow each refractory period 100, 106, 112, 118. Where thedetection profile meets the captured signal 104, a detected event isdeclared and a refractory period starts. Thus, when exponential decay102 meets the captured signal 104, a detected event is declared and arefractory period 106 starts. In the example shown, overdetection occurswhen the T-waves are detected, as occurs in association with refractoryperiods 106, 118 in addition to the R-waves associated with refractoryperiods 100, 112.

In the illustrative example of FIG. 5, the Estimated Peak is calculatedas the average of two previous peaks. As can be seen at 120, theEstimated Peak (represented as the height of the refractory periods 100,106, 112, 118) drops following the overdetection associated withrefractory period 106, as the newly calculated Estimated peak is anaverage of R-wave and T-wave amplitudes. This may increase thelikelihood of further overdetection by lowering the Estimated Peak to alevel that is closer to more signal peaks that represent potentialsources of overdetection.

Morphology Overdetection Identification

Some embodiments of the present invention provide example methods toidentify and correct overdetection. FIGS. 6A-6B, 7A-7B, and 8 presentmorphology-based approaches to identifying overdetection usingcorrelation. For illustrative purposes, these methods are applied to theoverdetection shown in FIG. 5.

Some illustrative embodiments of morphology overdetection identificationidentify alternating morphology patterns. For example, duringoverdetection, some events may correlate highly to a stored templatewhile other events may correlate poorly (indicating overdetections), inan alternating pattern. When a sequence of comparisons yieldsHigh-Low-High correlations, the pattern may be attributed tooverdetection. As shown below, the Low correlated detected events canthen be marked as overdetections. An alternating sequence is one type ofpattern, but other patterns may be sought instead. In another example,triple detection may be identified by the use of High-Low-Low triplets,and in yet another example, rather than a stored, static morphologytemplate, a series of detections may be compared one to another, makingeach new detection a separate template. Yet another example uses adynamic template that changes over time, for example, by integrating newdetections into the template, or by averaging a plurality of previouslydetected events.

Referring now to FIG. 6A, correlation waveform analysis is shown. Aportion of signal within the refractory period 100 in FIG. 5 is shown at130 in defined sample window 132. FIGS. 6A-6B show the unrectifiedsignal, while FIG. 5 shows the rectified signal. The sample window 132defines a number of samples 134, which are shown as a continuous linefor simplicity, as an understanding of “sampling” an analog signal intothe digital domain is considered to be within the knowledge of thoseskilled in the art.

The sample window also defines the alignment of the samples 134 around afiducial point (typically the maximum amplitude point) in the capturedsignal. Some illustrative methods for defining a sample window arediscussed in U.S. Pat. No. 7,477,935 entitled METHOD AND APPARATUS FORBEAT ALIGNMENT AND COMPARISON.

While some embodiments may use the refractory period to define thesample window 132, other embodiments tailor the sample window 132 usingfeatures of the template 136. In an illustrative embodiment, thetemplate 136 can be formed by analyzing one or more detected events toidentify QRS begin and end points, as well as their position relative toa fiducial point (such as the peak during refractory). These featurescan be used to define the stored template so it approximates the QRScomplex. Other template types may be used, including, for example, datatransforms and set reduction techniques. Some templates may also rely onmulti-channel sensing.

The samples within the sample window 132 are compared to the storedtemplate, which is graphically shown at 136. The specific mathematicalanalysis of a template comparison may vary. Morphology analysis mayinclude, for example and without limitation, Correlation WaveformAnalysis (CWA), reduced data set analysis includingpeak-feature-location identification and comparison, wavelettransformation, Fourier transformation, signal decomposition such assource separation, or other data analysis methods such as compressionmethods. For simplicity, in the following examples, reference is made tocomparison in the form of correlation/CWA, with the understanding thatthese other analysis methods may be substituted in other illustrativeembodiments. CWA may take use a simplified calculation of the sum ofabsolute values of differences between a template and a signal underanalysis, or CWA may use an approach in which the squares of differencesbetween signal samples and template samples are calculated and used tofind correlation. Simplified methods may be used to reduce computationalexpense.

With respect to the comparison in FIG. 6A, it can be noted that, asstated above, the signal at 130 comes from the refractory period at 100in FIG. 5, which corresponds to the R-wave of a cardiac cycle. As aresult, there is good correlation of the captured R-Wave to themorphology template.

FIG. 6B shows the signal 140 derived from samples adjacent the peak inthe refractory period 106 (FIG. 5) that occurs in association withoverdetection of a T-wave. As may be expected, the signal 140, aswindowed at 142, displays poor correlation to the stored template 136.Thus, the accurate detection in FIG. 6A shows good correlation to thestored template, while the overdetection in FIG. 6B displays poorcorrelation to the stored template. FIGS. 7A-7B and 8 illustrate howthese features of the overdetection in FIG. 5 may be used to identifyoverdetection.

Referring to FIG. 7A, a series of detections and associated refractoryperiods are displayed at 148, with the signal rectified. Unrectifiedsignal is shown at 150 including detected events 152, 154, 156. Forillustration, the events are numbered as shown at 148: event 156 is theN−1 event, event 154 is the N−2 event, and event 152 is the N−3 event.The event that is the most recent is shown at the far right of the eventdetection graphic 148. The detections 150 correspond to an R-wave 152, atrailing T-wave at 154, and another R-wave at 156. Sample windows 160,162, 164 are defined for each detection 152, 154, 156. In the example,the fiducial point of each sample window is shown as a vertical line.The fiducial point is offset to the left of the sample windows 160, 162,164; an offset fiducial point may be used but need not be the case.

Next, the signal samples within each sample window 160, 162, 164 arecompared to a template 172, as shown at 170. The comparison outcomes areshown as percentage correlations, indicated at 174. As shown, the scorefor R-wave 152 is high (95%), indicating strong correlation to thetemplate 170. This makes R-wave 152 “Similar” to the template, asindicated. Likewise, the score for R-wave 156 is high (90%), againindicating strong correlation to the template 170, thus, the “Similar”marking However, the overdetected T-wave 154 does not correlate well tothe template 170, and has a low correlation score (5%) and is marked“Dissimilar”. The numbers provided in FIG. 7A at 174 are provided onlyfor illustration and are not the result of actual computations.

Following calculation of scores at 174, the method next characterizeseach score, as indicated at 182. An illustrative characterization methodis shown in FIG. 8. Referring to FIG. 8, CWA is referenced, with scoresprovided on a scale from 0-100%. Three zones of comparison are shown at184, 186, and 188. Scores falling within the first zone 184 areconsidered dissimilar from the stored template, while scores fallingwithin the third zone 188 are considered similar to the stored template.The second zone 186 is treated as a hysteresis band in which events aremarked the same as the prior event, for example, an event falling withinthe second zone 186 that follows an event falling in the third zone 188would be marked “similar”. In an illustrative example, the boundarybetween the first and second zones 184, 186 is set to about 25%correlation, while the boundary between the second and third zones isset to about 52% correlation. Other boundaries and/or forms of thisanalysis to mark similar and dissimilar events relative to a templatemay be used.

Referring back to FIG. 7A, the comparison scores 174 are characterizedas shown at 182. The second detection 154 is a T-wave and, due to a lowcomparison score, is marked “Dissimilar”, while the other two detectionsare marked as “Similar.” The “Similar” and “Dissimilar” markings areused for applying a comparison overdetection rule which is shown in FIG.7B. The rules rely in part on the pattern shown at 190, in which theevents N−1, N−2, N−3, form a similar-dissimilar-similar pattern. Thereare two parts to the comparison overdetection rule:

-   -   A) As shown at 192, an alternating pattern 190 is sought; and    -   B) As shown at 194, the N−3 detection must score “High” and        above the Hysteresis zone 186 in FIG. 8.        As can be appreciated from the manner in which events are        marked, rule 194 effectively ensures that none of the three        detections (N−1, N−2, N−3) has a correlation score that falls        into the hysteresis zone 186.

As indicated at 196, if both rules are met, then the method marks one ofthe events (N−2) as a Morphology Overdetection. In the illustrativeexample, the analysis contemplates events N−3, N−2 and N−1. The timingof analysis (using events N−1, N−2, and N−3 but not event N) is merelyillustrative, and the present invention is not limited to any particulararchitecture with respect to the timing of analysis of individualevents.

The use of the overdetection marker is further discussed below withreference to FIGS. 18-21. Generally speaking, the method in FIGS. 18-21does not differentiate Morphology Overdetection from other detections,however, if desired, the treatment of overdetection(s) may varydepending upon the identifying method. In another embodiment, datarelating to which type of Overdetection Analysis has identifiedoverdetection(s) may be retained to help analyze device operation, forexample, to allow refinement of the detection profile and/or theOverdetection Analysis.

In addition to the morphology analysis, interval timing may beconsidered. In one embodiment, the Morphology Analysis Overdetection isomitted if the intervals between the three detections are greater than athreshold, such as 500-1000 miliseconds. For example, it may be foundthat detections more than 800 milliseconds apart are unlikely to resultfrom overdetection, or that an implantable system is unlikely to makeany incorrect therapy decisions based on overdetection resulting in 800millisecond intervals (equal to 75 beats-per-minute).

Alternating Interval Overdetection Identification

As indicated by FIGS. 1-2, another illustrative method for identifyingoverdetections uses event intervals to identify alternating intervalpatterns. It is believed that overdetection may be identified byanalyzing the intervals between detected events. If analysis of a set ofdetected events indicates an alternating pattern of long-short intervalsbetween events, overdetection may be occurring.

FIG. 9 provides an illustration of an alternating long-short-longinterval pattern. In particular, a captured signal is shown at 200. Adetection profile similar to that of FIG. 4 is applied to the capturedsignal 200. The result is consistent overdetection, with an R-wavedetection shown in association with a refractory period at 204, and aT-wave detection shown in association with a refractory period at 206.This pattern repeats with detections associated with refractory periodsat 208 and 210.

The intervals from detection to detection are shown and characterized at212, including short intervals 214 and long intervals 216. In a numericexample, if the refractory periods are about 100 ms, then the shortintervals may be in the range of 200 ms, while the long intervals are inthe range of about 450 ms. This would result in a detected heart rate ofabout 184 beats-per-minute (bpm) with an actual cardiac rate of only 92bpm, with the difference being attributed to persistent overdetection. Adifferent duration for the refractory period may be used.

The long-short-long pattern provides another basis for identifyingoverdetection. The pattern may become more difficult to discern athigher rates, since the difference between long intervals 216 and shortintervals 214 becomes less apparent. If desired, and as shown in FIG.16, the alternating interval pattern analysis may be omitted whendetected heart rates become relatively high.

FIG. 10 illustrates an alternating interval pattern. At 220, a mappingof interval durations is shown, with a center line shown at 222 as theaverage interval. Any suitable number of intervals may be used tocalculate the average. A voidband having high and low boundaries isshown, with the short interval boundary shown at 224 and a long intervalboundary shown at 226. The voidband is defined by a voidband constant inthe example shown. Thus, for example, if the four interval average at agiven point in time is 400 milliseconds (150 bpm), and a voidbandconstant of about 23 milliseconds is used (other voidband constants maybe used), then the boundaries 224, 226 would be 377 milliseconds and 423milliseconds, (142 to 159 beats bpm) respectively. A different voidbanddefinition may be used instead, for example, simply +/−10 bpm, or anoffset such as +10 milliseconds, −20 milliseconds.

In the illustrative example, in order to identify an alternatinginterval pattern, several rules are applied. First, the four intervalaverage 222 must fall within a predetermined range, as indicated at 230.Some embodiments omit rule 230. Second, a specific pattern must befound, as indicated at 232. In the illustrative example, pairs ofconsecutive intervals are considered, and there must be at least sixcrossings of the voidband by a line drawn between each interval withinthe previous 8 intervals. Crossings of the voidband in the illustrativeexample of FIG. 10 are shown and numbered at 234. For example, intervalI_(n-5) is longer than the boundary 226, and interval In−4 is shorterthan the duration defined by boundary 224. Thus, the pair I_(n-5),I_(n-4) crosses the voidband, increasing the count of interval pairsthat satisfy the specific pattern 232. Other parameters may be used toidentify the alternating intervals, if desired.

Another rule is shown at 236 and calls for a Long-Short-Long pattern inthe three most recent intervals. Referring to FIG. 9, it can be seenthat, when a Long-Short-Long pattern forms due to overdetection ofT-waves, the Short interval likely corresponds to the time from theR-wave detection to the T-wave detection. The rule 236 calls foridentifying a set of Long-Short-Long intervals in the intervals for N−1to N−3.

As shown in FIG. 10 at 238, if each rule 230, 232, 236 is met, thendetection N−2 is marked as an alternating interval overdetection. Theincorporation of overdetection marking into a rate calculation method isfurther illustrated by FIGS. 18-21, below. FIGS. 18-21 illustrateselective correction of data for rate calculation; however, such datacorrection could interfere with the Alternating Interval OverdetectionIdentification method by combining intervals, removing the shortintervals and preventing the noted voidband crossings. Within thealternating interval analysis, the identification of an overdetectiondoes not change the handling of detected events. Therefore, in anillustrative example, Alternating Interval Overdetection Identificationmethods make use of raw, uncorrected intervals (based on detections thatpass waveform appraisal) to establish the Average Interval and toidentify excursions below and above the voidband, rather than usingcorrected intervals.

Analysis as shown in FIG. 10 is one example of an Alternating Intervalanalysis. Other Alternating Interval analysis may look for other time orinterval based patterns in a queue of intervals between detected events.Examples include triple detection (long-short-short triplets),combinations (sets where three intervals are captured, with the secondand third interval being approximately as long as the first interval,indicating a correct detection followed by a double detection pair), orany other suitable timing-based pattern analysis.

Wide Complex Overdetection Identification

Some embodiments of the present invention are directed towardidentifying overdetection of wide QRS complexes. FIGS. 11, 12A-12D,13A-13B, and 14 illustrate Wide Complex Overdetection Identification.The Wide Complex Overdetection Identification methods observe whetherdetections occur within short intervals and with predeterminedmorphology characteristics. If the proximity and morphologycharacteristics are identified, the Wide Complex OverdetectionIdentification method determines that overdetection has occurred.

Referring now to FIG. 11, the unrectified signal is shown as signal 290.The signal 290 demonstrates a wide QRS complex. The rectified version ofthe signal 290 is shown at 300. As can be seen at 302, 304, the wide QRSis detected twice. This pattern repeats itself again at 306, 308.

FIGS. 12A and 12B show two rule combinations that can be used, eitheralone or as alternatives to one another, for identifying wide complexdouble detections. In FIG. 12A, at 320, detections N−1 and N−2 areshown. For purposes of applying the rule set, positive and negativepeaks of each detection are marked as “p+” and “p−”, respectively. Thepositive peak, p+, is marked at the point of maximum (or most positive)signal amplitude, and the negative peak, p−, is marked at the point ofminimum (or most negative) signal amplitude during each refractoryperiod.

FIG. 12A shows a first wide complex rule set. A first rule is shown at322 and is labeled the Detection Interval Rule. The first rule 322 callsfor the interval between the detections (shown as t₁ 324) to be lessthan a predetermined value, noted as Rule_(—)1_Duration. The second rule326 is labeled the Peak Proximity Rule and calls for a time t₂ 328 thatis the duration between the latter peak (here, p−) of the N−2 detectionand the earlier peak (here, p+) of the N−1 detection to be less thananother predetermined value, noted as Rule_(—)2_Duration (it should benoted that time is not shown to scale).

In an illustrative example, Rule_(—)1_Duration is set to about 195milliseconds. In another illustrative example, Rule_(—)1_Duration is setto the sum of the duration of the refractory period plus about 40milliseconds. In an illustrative example, Rule_(—)2_Duration is set toabout 20 milliseconds. Other values may be used for Rule_(—)1_Durationand Rule_(—)2_Duration. Some examples set Rule_(—)1_Duration within arange of 150-240 milliseconds, or, in other examples, the refractoryduration plus 20-60 milliseconds. Some examples set theRule_(—)2_Duration in the range of 10-40 milliseconds. Otherformulations may be used as well.

FIG. 12B shows a second wide complex rule set. In FIG. 12B, at 330, aset of detections N−1 and N−2 are shown, with positive and negativepeaks marked with p+ and p− indicators. A first rule is shown at 332 asa Detection Interval Rule in which the interval t1 334 between thedetections is compared to Rule_(—)1_Duration. The second rule is shownat 336 and is referred to as a Polarity Rule. The Polarity Ruledetermines whether the N−1 and N−2 detections are of opposing“polarity.” For the purposes of the Polarity Rule, a detection isconsidered as having positive polarity if the p+ peak occurs before thep− peak; otherwise, the detection is negative. If the polarities of thetwo detections N−1 and N−2 are not the same, as shown, then the secondrule 336 is met.

The signals shown in FIGS. 12A-12B are simplified to highlight the useof p+ and p− markers for identifying peak proximity and polarity. FIGS.12C and 12D provide examples simulating more realistic signals in whichwide QRS complexes are overdetected.

FIG. 12C illustrates application of a Wide Complex rule set to anoverdetected signal having a wide QRS complex. The rectified version ofthe signal is shown in the upper portion of FIG. 12C to illustratedetections 340 and 342 occurring as the wide complex is overdetected.The unrectified signal is shown at 344. For a one-sided signal as shown,the negative peak p− can be defined as the lowest amplitude sample. Thefirst detection 340 has p− occurring before p+. By definition, thisgives the first detection 340 negative polarity.

For the second detection, the p+ occurs first, giving the seconddetection positive polarity. Because the first detection has negativepolarity and the second detection has positive polarity, the polarityrule is met. As noted, the detection interval rule is met. As a result,both the first and second rules noted in FIG. 12B are satisfied by thedetected event pattern shown in FIG. 12C.

FIG. 12D illustrates application of another Wide Complex rule set toanother signal. Again two detections 350, 352 are shown in rectifiedform for detection purposes. In FIG. 12D, the alternating polarity rulefails because the first event 350 and second event 352 both havepositive polarity, with the positive peak of each occurring first.Meanwhile, the detection interval rule is met. In the example, the peakproximity rule is met because p− for the first detection 350 is near theend of the refractory period, while p+ for the second detection 352 isnear the start of the refractory period.

Rule sets are met in each of FIGS. 12C-12D. FIG. 13A shows howsatisfaction of a rule set may be handled. FIG. 13B shows how events canbe marked for purposes of rate calculation in FIGS. 18-21 using the ruleset results and other conditions.

As shown in FIG. 13A, the Wide Complex Overdetection Identificationmethod uses “True” and “False” markers for individual detected events.These markers indicate the confidence the system has in the individualdetections. A “False” marking indicates a lack of confidence in a givendetection, meaning that the analysis of the Wide Complex OverdetectionIdentification method has found that the False detection is likely anoverdetection. A “True” marking indicates that the wide complex analysishas not identified a given detection as a likely overdetection. If largenumbers of detections are marked False, overdetection is suspected. FIG.22 provides an example of a charge confirmation method that may be usedto verify therapy decisions before preparations for therapy delivery aremade if large numbers of detections are marked False.

The True-False marking of FIG. 13A may be performed regardless of heartrate. In an illustrative example, the additional marking shown in FIG.13B of individual events as Wide Complex Overdetections and/or WideComplex Suspect is performed only while the detected rate is in apredetermined range (FIG. 16). This heart rate range limit on WideComplex Overdetection marking and/or Wide Complex Suspect marking may beomitted in some embodiments.

Referring to FIG. 13A, the illustrative example shows how events may bemarked given initial circumstances and rule outcomes. For example, asshown at 362, when event N−2 is True and no rule set (FIGS. 12A-12B) issatisfied, then N−2 remains True and N−1 is newly marked as True.Another circumstance is shown at 364, which begins with N−2 marked asTrue. In this circumstance, a rule has been met, a morphology templateis available to the system, and the correlation of event N−1 to themorphology template is better (higher CWA score in the illustrativeexample) than the correlation of event N−2to the morphology template. Insuch a circumstance 364, event N−2 has its marker changed from True toFalse, while event N−1 is marked True. As illustrated by thecircumstance at 364, the marking of an event as True is sometimes only apreliminary determination which may be changed later in the analysis.

Next, as shown at 366, in any other circumstance in which event N−2starts True and a rule set is met, the result will be a marker of Truefor event N−2 and a marker of False for event N−1. Finally, as shown at368, if the initial circumstance is that N−2 has been marked False, thenevent N−1 is marked True without consideration of the outcome of theapplication of the rule sets of FIGS. 12A-12B.

Referring to FIG. 13B, illustrative handling of True-False markers isshown. The handling relies, in part, upon the status of the system asshown at 380 and 390. A “Pattern Found” or “No Pattern” state resultsfrom identification of a detection pattern that indicates wide complexoverdetection. Illustrative examples of patterns that can be used toidentify “Pattern Found” and “No Pattern” states are shown below.

As shown at 380, a first system state is one in which the calculatedheart rate is in a predetermined range and a pattern has been found.When in this state, the method assigns wide complex overdetectionmarkers to selected events. In the illustrative example, when detectedevents N−3, N−2, and N−1 form a True-False-True sequence, then a widecomplex overdetection marker is assigned to N−2. Otherwise, as shown at384, no wide complex overdetection marker is assigned. The use of theoverdetection marker is further explained with reference to FIGS. 18-21.

As shown at 390, a second system state occurs in which the rate is inrange but the system is not in a pattern found state. As shown at 392,when detected events N−3, N−2, and N−1 form a True-False-True sequence,event N−2 is assigned a suspect event marker. The use of the suspectevent marker, again, is explained further with reference to FIGS. 18-21.In any other combination, no WC suspect marker is assigned, as shown at394.

As noted at 380, 390, “Pattern Found” and “No Pattern” states aredefined, therefore some illustrative pattern searching examples areshown next. Generally the approach is to identify particular features ofthe overall rhythm, encompassing several detected events, which indicatethat a pattern of Wide Complex overdetection appears likely. When suchparticular features are identified, a “Pattern Found” state can beinvoked, allowing events to be marked as overdetections.

A first example of a pattern that may be used to define the “PatternFound” and “No Pattern” states has been shown above in FIG. 10 as analternating interval pattern. Different heart rate ranges may be usedfor Wide Complex Overdetection Analysis and Alternating Intervalanalysis, as indicated in FIG. 16, below. Thus, in the illustrativeexample, when the rate is in the Wide Complex range and the other rules(rules 232, 236) for an alternating pattern from FIG. 10 are met, the“Pattern Found” state is entered.

Other patterns may also be used to establish a “Pattern Found” state.One example uses alternating Wide Complex Suspect (WC Suspect) eventmarkers. An alternating pattern could be: [WC Suspect]-[Not Suspect]-[WCSuspect]-[Not Suspect]. Such a four event pattern can be sufficient toenter the Pattern Found state. In one illustrative example, only suspectmarkers generated by the Wide Complex Overdetection method are used toidentify the alternating suspect event marking. In another example, alarger set of events is used to establish the pattern, and/or any sourceof suspect event markers may be relied upon to establish the pattern.

FIG. 14 graphically illustrates transitions between system states. Theexample in FIG. 14 includes two in-range states and an out of rangestate. In each state, the system performs True-False marking as set outin FIG. 13A. The True-False marking may be used in later steps such ascharge confirmation shown in FIG. 22.

The illustration 400 provides for an Out of Range state 402, in whichwide-complex suspect and overdetection marking is off (WC Off). The Outof Range state 402 is effective when the detected heart rate fallsoutside of a predetermined range. When the heart rate enters the range,the system leaves the Out of Range State and enters an In Range, NoPattern State 404.

Once in the In-Range, No Pattern State 404, the system begins lookingfor T-F-T sequences and, if any are found, suspect event marking asshown at 390-392-394 in FIG. 13B takes place. The system also looks forpatterns that indicate overdetection is occurring. This may includeobserving a pattern of alternating long-short intervals and/or a patternof WC Suspect event markers. In one example, a pattern as shown in FIG.10 is sought. Once both the rate range and a pattern are found, thesystem then transitions to an In-Range, Pattern Found state 406.

Once in the In-Range, Pattern Found state 406, if a T-F-T pattern isfound, the system assigns a Wide Complex Overdetection marker asexplained at 380-382-384 in FIG. 13B. A transition from the In-Range,Pattern Found state 406 to the In-Range, No Pattern state 404 may occurif a timeout occurs without any Wide Complex Overdetection markers beingassigned. In one illustrative example, if 64 consecutive detected eventspass without any wide complex overdetection markers being assigned, thepattern is considered lost and the system transitions from state 406 tostate 404. Use of N=64 is merely illustrative, and other thresholds maybe used.

Within the illustration 400, from either In-Range state 404, 406, if thecalculated rate falls outside of the rate range, the system returns tothe Out of Range state 402. In an alternative embodiment, the system maywait to begin suspect event or overdetection marking until a pattern hasbeen found in addition to meeting the rate range. In yet anotherembodiment, rather than entering the In Range, No Pattern state 404 whenthe rate enters the predetermined range, the method may assume a patternexists and enter the In-Range Pattern Found state 406 immediately uponmeeting the rate range condition.

While in the Out Of Range state 402, an illustrative method does notperform either WC Suspect or WC Overdetection marking as shown in FIG.13B. If desired, True/False marking may be omitted while in the Out ofRange state 402. In one example, however, True/False marking isperformed and may be used, upon transition into the rate range, toimmediately enter the In-Range, Pattern Found state 406. In anotherexample, True/False marking is performed at all times, and a buffer ofTrue/False markers and event polarity indications is kept in order toprovide information for use in Charge Confirmation methods shown in FIG.22. Event width and correlation scores may be carried forward as well.

Thus, FIG. 14 provides an illustration of system operation byintegrating the True-False marking and suspect and/or overdetectionmarkers of FIGS. 13A-13B, which in turn apply the rules of FIGS.12A-12D.

The above rules indicate that, following a False event marker, the nextevent is marked True (Rule 368 in FIG. 13A). However, the marking of anN−1 event as “True” is a preliminary indication. During a next iterationof the method, an event that was marked True when in the N−1 analysisslot may be marked false when it is in the N−2 analysis slot, as mayoccur as shown by FIG. 15.

FIG. 15 shows analysis of four events, a, b, c, and d, which areconsecutively occurring detected events that each have passed waveformappraisal. As shown at 450, at time t1 events a and b are treated asevents N−2 and N−1, respectively, for the rule analysis of FIG. 12A-12B.As shown by FIG. 13A, at 366, when the rules are met and the earlierevent, N−2 does not have lesser correlation to the stored template thanthe latter event, N−1, the N−1 event (event b) is marked False, whilethe N−2 event (event a) is marked True.

The method then iterates to 452, where, at time t2, events b and c aretreated as N−2 and N−1, respectively, and the rules would again beapplied. Here, because event b is already marked as False, event c isautomatically marked as True based on the rule shown in FIG. 13A at 368.If a Wide Complex rate range is met, the T-F-T pattern will result inthe N−2 event (event b) being marked as either a Wide ComplexOverdetection or as Wide Complex Suspect, depending on whether a PatternFound state is in effect. The method next iterates to 454.

As shown at 454, events c and d are treated as N−2 and N−1,respectively, and the rules of FIG. 12A-12B are applied. As indicated,one of the rule sets of FIGS. 12A-12B is again met. In the illustrativeembodiment, the correlation to the stored morphology template of thelatter event, N−1, is greater than the correlation of the earlier event,N−2. As per rule 366 in FIG. 13A, the N−2 event is marked False and theN−1 event is marked True. The result of this analysis is the marking ofconsecutive events b and c as False. Note that at this point, an F-F-Tpattern has developed. No Wide Complex Overdetection or Wide ComplexSuspect event marker is applied to the N−2 event (event c), since thispattern is not one of the marker patterns shown in FIG. 13B. Theconsecutive False markers do, however, reduce confidence in eventdetection accuracy. FIG. 22, below, provides further illustration ofmarking and analysis for charge confirmation that can add persistencefactors to therapy decisions when too many events are marked False. Inyet another embodiment, if desired, a pairing of F-F may be analyzed todetermine whether the two detections indicate a triple-detection patternhas occurred. For example, consecutive False markers may result in acalling of a morphology analysis to determine whether an immediatelypreceding or following event matches a stored or dynamic template.Alternatively, if a T-F-F-T sequence is identified, the two detectionsmarked as True may be compared one to another; high correlation mayindicate triple detection has caused the intervening False events.

Integration, Data Correction and Charge Confirmation

FIG. 16 graphically illustrates an integration of several overdetectionanalysis methods. In the illustrative example 470, waveform appraisal472 may be enabled at any detected event rate, as is MorphologyOverdetection Analysis 474, if a template can be established for use inthe analysis.

As shown at 476, Wide Complex Overdetection Identification Analysis isenabled in a relatively higher rate zone, with Alternating IntervalOverdetection Identification Analysis enabled in a lower rate zone at478. In some embodiments, an upper limit is placed on the Wide ComplexAnalysis 476, for example, in the range of 405 bpm calculated heartrate, and the border between the Wide Complex Analysis 476 andAlternating Interval Analysis 478 is set in the range of about 160 bpm.

These variables may change or may be omitted. The upper and/or lowerrate limits on Wide Complex Analysis 476 may be omitted, for example, aswell as the upper limit on Alternating Interval Analysis 478. Also,rather than a strict “border,” these analysis zones may overlap.Transitions may also take into account various hysteresis factors suchas, but not limited to, crossing the “border” by an amount greater thansome value (i.e. 20 ms or 20 bpm beyond the border) and/or meeting therequirement for a selected number of consecutive detected events.

FIG. 17 illustrates the use of a refined detection profile. The purposeof FIG. 17 is to show that, for any given profile, it is likely possibleto identify an implantee in whom the profile may result inoverdetection. The illustrative profile is similar to one of those shownin U.S. Provisional Patent Application No. 61/034,938, entitled ACCURATECARDIAC EVENT DETECTION IN AN IMPLANTABLE CARDIAC STIMULUS DEVICE, filedon Mar. 7, 2008. As shown above the label “Overdetecting T-Waves”cardiac cycles shown at 510, 512 are double counted as both the R-wavesand the trailing T-waves lead to detections. Further, as shown above thelabel “Overdetecting Wide Complexes,” the profile also double detectsthe QRS complexes shown at 520, 522. Refining the detection profile maynot avoid all overdetection.

FIGS. 18-21 provide illustrations of the handling of Overdetection andSuspect event markers from the above illustrative examples. FIG. 18provides an example of what happens during “normal” detection, when noevents are marked as suspect or overdetections. A buffer of detectionsand associated intervals is shown at 600. The definition of detectionand interval is indicated at 602: a detection threshold crossing is adetection, and an interval is the period of time between consecutivedetections.

As shown at 600, detections and intervals occur in an ongoing series,with a most recent detection shown at 604, separated by a most recentinterval 606 from a second most recent detection 608. For illustrativepurposes, the examples in FIGS. 18-21 operate using a delay of at leastone event; a real time system that analyzes event 602 as soon as it isdefined could be used instead.

As shown at 610, an analysis window is defined to perform analysis onthree detected events and associated intervals. As the analysis 610 iscompleted, detected events are marked as certified detected events 612.In addition, an interval is marked as a certified interval if no suspectevent or overdetection markers are applied to events defining theinterval. The newest certified interval is introduced into a first-in,first-out (FIFO) buffer of certified intervals 614, as indicated by line616.

In the illustrative example, four certified intervals 614 are used inthe FIFO buffer to calculate a 4RR Average 618, which is used to find acalculated heart rate 620 for the system. In the illustrative example,until intervals are certified, they are not used in rate calculations.The analysis 610 will “certify” intervals for use in rate calculationunless the interval is marked suspect or is combined as a result of adetection being marked as an overdetection. The analysis 610 may includeany of the above analyses such as waveform appraisal, morphologyoverdetection analysis, alternating interval overdetection analysis,and/or wide complex overdetection analysis.

FIG. 19 shows analysis when a suspect event marker is applied. A suspectevent marker is shown in the above examples as a possible outcome ofWaveform Appraisal analysis or Wide Complex Overdetection Identificationanalysis. Within the series of detections and intervals 640, theanalysis window is shown at 642. An event at 644 is marked as a suspectevent.

In operation, the suspect event 644 is known to be unreliable, but it isnot known whether the suspect event 644 is, for example, an R-wavemasked by spurious noise, a double detection, or a detection caused byexternal noise. Since the source of event 644 is unclear, as indicatedby its marking as suspect, each interval defined by the event 644,including both intervals 646 and 648, is determined to be unreliable forrate calculation. The method does not pass intervals 646, 648 to thebuffer of certified intervals 650 which is used to generate the 4RRaverage 652 and hence the rate 654. Instead, prior intervals that havealready been certified are retained in the buffer 650 until a newinterval is certified. For example, if neither of the detections oneither side of interval 656 are marked as suspect or as overdetections,then interval 656 will pass to the buffer 650 once analysis 642 hasmoved on, as indicated by 658.

FIG. 20 shows treatment of overdetection markers. In the example shown,persistent overdetection is marked within the series of detections andintervals 700. The analysis window is shown at 702, and overdetectionmarkers are shown at 704. In the illustrative example, data is correctedwhen an overdetection marker is applied. More specifically, intervalsaround an event having an overdetection marker are combined, and theevent itself is discarded.

For example, an overdetection marker is applied to the detection at 706.The intervals 708, 710 on either side of detection 706 are combined intoa single interval 712. Detection 706 may also be discarded, for example,removing it from estimated peak calculation(s). This combined interval712 is brought into the certified interval buffer 720. Likewise, acombined interval at 714 enters the buffer 720. As the analysiscontinues, the combined interval shown at 716 will also be added to thebuffer 720 and used to generate the 4RR Average 722 and rate 724.

FIG. 20 provides a contrast to FIG. 19. When a detection is markedsuspect, as in FIG. 19, it is not known whether an associated cardiaccycle has been counted yet. An overdetection 706, when identified,likely corresponds to a cardiac cycle that has already been counted byanother detection. Therefore, data correction by combining intervals708, 710 into combined interval 712 is determined to be appropriate.

It should be noted that for either of FIG. 19 or FIG. 20, in addition tomodifying the rate calculation, applying a suspect event oroverdetection marker may also change the calculation of an estimatedpeak. As noted, overdetections or suspect events may sometimes lower theestimated peak and increase the likelihood of further overdetection.When a suspect event or overdetection marker is applied, someembodiments exclude one or more detected events from calculation of theestimated peak. In one illustrative example, if two previous peaks wouldusually be averaged to calculate estimated peak, if an overdetection orsuspect event marker is applied, the larger peak of the two peaks may beused as the estimated peak.

FIG. 21 combines the above analysis of FIGS. 19-20 by showing acircumstance in which both suspect event and overdetection markers areapplied. In the data 750, a suspect event marker has been applied at752. This results in intervals shown at 754 being marked as suspect andtreated as unreliable and unusable.

Also in FIG. 21, the detection at 756 is marked as an overdetection.This detection 756 is then discarded and the associated intervals 758,760 are combined into a single interval 762. The combined interval 762is used in the buffer of certified intervals 764, which is used tocalculate the 4RR Average 766 and heart rate 768.

FIG. 22 provides an example of charge confirmation analysis. The methodof FIG. 22 is largely directed toward analyzing whether therapy isproper at a time when the cardiac rhythm is likely malignant. Theanalysis of FIG. 22 is largely an effort to avoid improper therapydelivery to an implantee when overdetection is occurring. The method inFIG. 22 may be performed as part of a therapy decision, for example, asshown at 22 in FIG. 1 or 48 in FIG. 2. The analysis shown in FIG. 22includes a Start Block which would begin with an internal variable,denoted “Tolerance” in the Figure, is initialized to zero, and the StartBlock would be called once other factors (such as the X/Y andpersistence conditions noted above) are already met.

FIG. 22 makes use of two further data sets. First, individual events aretagged as 0, 1 or 2 using the True-False and polarity designations fromFIGS. 12A-12D and 13A, as follows:

-   -   If marked True, the event receives tag 0.    -   If marked False and having positive polarity, the event receives        tag 1.    -   If marked False and having negative polarity, the event receives        tag 2.

Several counters are then generated from a buffer of the 16 most recentdetected events that have passed waveform appraisal (this would includeevents marked as overdetections and/or suspect by methods other thanwaveform appraisal), as follows:

-   -   Total_WC_Beats: Number of Wide Complex Suspect or Wide Complex        Overdetection marked detections in the buffer    -   Max Cons 01: Maximum number of consecutive 0-1 tag combinations    -   Max Cons 02: Maximum number of consecutive 0-2 tag combinations        These calculated variables are then used as shown in FIG. 22.

Beginning at a start block 800, the method determines if Total_WC_Beatsis less than six, as shown at 802. If not, the method checks whetherTotal_WC_Beats is greater than or equal to eight, as shown at 804. Ifso, the method determines whether Max_Cons_(—)01 is greater than orequal to three, as shown at 806. If not, the method determines whetherMax_Cons_(—)02 is greater than or equal to three, as shown at 808. Ifnot, the variable “Tolerance,” which is an integer variable created foruse as a persistence factor in the flow diagram of FIG. 22, is comparedto five. If Tolerance is greater than or equal to five, as shown at 810,the charge confirmation method is satisfied, and the method returns anindication to start charging, as shown at 812, for the purpose ofcharging high power capacitors for use in delivering therapy.

Going back to step 810, if Tolerance is not equal to or greater thanfive, the method goes to block 814, where Tolerance is incremented andthe method returns an indication that charging should not be started, asindicated at 816. Setting the Tolerance limit as five is merelyillustrative, and larger or smaller settings may be used.

Continuing back through the method to capture alternative outcomes, ifeither of blocks 806 or 808 returns a Yes result, then the method resetsthe Tolerance variable to zero, as shown at 818, and the method returnsan indication that charging should not be started, as shown at 816. Theconsecutive pairings of 0-1 or 0-2 that trigger reset of the Tolerancevariable from blocks 810 and/or 812 indicates repetitive doubledetections having similar morphology over time. Resetting the Tolerancevariable is allowed in each of these circumstances at least because arequisite level of polymorphic behavior that would be associated withventricular fibrillation and/or highly discordant arrhythmias (such aspolymorphic ventricular tachycardia) is not occurring. Such judgmentsregarding the cardiac rhythms that will or will not be treatedaggressively may vary in some embodiments or in response to physicianpreference.

In some embodiments, a triple detection identification method may becalled in addition to the other overdetection identification methodsshown herein. The use of True-False and 0-1-2 marking shown above mayprovide analytical tools for such triple detection identification. Inone such embodiment, triple detection patterns are identified byobserving whether a pattern of 0-1-2 or 0-2-1 repeats, such as{0-1-2-0-1-2-0 . . . }, and data correction to remove each of the 1 and2 detections can be performed. Such an embodiment may include analysisof the True (0) detections to determine whether narrow QRS features canbe identified.

In block 802, the Yes result likely indicates a shockable rhythm such asventricular fibrillation. Therefore, the method goes directly to block816 and returns a result indicating that charging should begin. Thisbypass of the “Tolerance” analysis may be omitted in some embodiments.Finally, if block 804 returns a No result, then the checks at 806 and808 are determined to be unnecessary, and the method skips to block 810where the Tolerance variable is checked.

In an illustrative example, the charge confirmation method shown in FIG.22 is used as a prerequisite to initiating charging of high-powercapacitors in an implantable cardioverter-defibrillator or otherimplantable therapy delivery system. Once capacitor charging begins, inan illustrative example, the method of FIG. 22 is no longer invokeduntil a shock is delivered or the episode terminates.

FIG. 23 shows an example of analysis. Some analysis methods take anapproach in which a series of buffers are filled during analysis towarda decision to deliver therapy to a patient. For example, cardiac ratemay be measured and, once calculated as tachyarrhythmic, a counterbegins to determine how many consecutive rate calculations occur havingthe tachyarrhythmic rate. Once the tachyarrhythmic rate counter isfilled, a tachy condition is met and the device will perform additionalmorphology analysis to determine whether the patient is showing amonomorphic rhythm and/or whether the patient's individual detectedevents are not correlated to a stored template. In this example,morphology analysis occurs at the end of the analysis. By only using themorphology analysis at the end of the analysis, it is underemphasized.With morphology only at the end of the analytical method, incorrecttherapy decisions may not be avoided.

In contrast, the method of FIG. 23 is shown at 900 using a differentorder. In particular, the method 900 follows event detection 902 withwaveform appraisal 904, in which the detected event is analyzed byitself to determine whether it likely is caused by, or is masked by,noise. As suggested above, waveform appraisal 904 may take a form asshown in U.S. Pat. No. 7,248,921, titled METHOD AND DEVICES FORPERFORMING CARDIAC WAVEFORM APPRAISAL. Next, morphology qualificationtakes place, as shown at 906. Morphology qualification 906 includes oneor more of the double detection methods shown above, such as widecomplex overdetection, morphology overdetection, and alternatinginterval overdetection.

Next, the rate is estimated, as shown at 908. The rate is characterizedas falling into one of three zones: A VF zone, a VT zone, and a Lowzone. The VF zone is a high zone, typically greater than 180 bpm, andsometimes higher than 240 bpm, for example. The Low zone is anon-malignant zone, for example, below 140 bpm, though possibly reachingto as high as 170 bpm for some patients, and even higher, particularlywith younger patients. The VT zone is defined between the Low zone andthe VF zone. In this example, “VT” and “VF” are simply labels, notdiagnoses. The rate estimate may make use of the methods shown abovethat correcting data in response to identified overdetection.

If the rate is Low, the detected event is marked as not shockable, asindicated at 910. If the rate is in the VT zone, an optional detectionenhancement 912 may be called. In an illustrative example, detectionenhancement 912 includes a tiered analysis in which the detected eventunder consideration is compared to a static template. If the detectedevent correlates well with the static template, the detected event ismarked as not shockable 910. If the event does not correlate to thestatic template but does correlate well to a dynamic template formed ofan average of four recently captured events and shows a narrow QRScomplex (the combination suggests a monomorphic tachycardia havingnarrow complexes), the method will also proceed to step 910. Otherwise,if detection enhancement 912 fails, the detected event is marked asshockable, as shown at 914.

If the rate calculated at 908 is in the VF zone, detection enhancement912 may be bypassed and the detected event is marked as shockable asshown at 914. In one embodiment, the implantable device is programmed toset the boundaries of the VT zone and VF zone and/or to omit the VTzone. In yet another embodiment, the VF zone may be omitted, and allrates above the Low zone would be directed through detection enhancement912.

The markings of shockable and not shockable are maintained in an X/Ycounter, which provides an initial counter for determining whether toproceed to therapy. If the X/Y counter fails (counters such as 12/16,18/24, or 24/32, for example, may be used), then no therapy is appliedand the method of analysis ends with no shock 918. The system then waitsto call the method again when the next detection occurs. The X/Y counter916 may also integrate a persistence factor, for example, calling forthe X/Y counter condition to be met for a series of consecutive detectedevents.

The illustrative method 900 also calls a charge confirmation check, asshown at 920. The charge confirmation check 920 may be as shown above inFIG. 22. The charge confirmation check, if passed, leads to the decisionto charge and shock 922. Charge and shock 922 may be called withanalysis continuing to ensure that the patient's malignant rhythm doesnot correct itself If the patient's malignant rhythm returns to normalbefore a shock is delivered, the method may terminate the charge andshock sequence 922. If the charge confirmation check 920 does not pass,the method again ends at 918 and waits for the next detected event.

The method shown in FIG. 23 is separable from the other methods shownabove for identifying overdetections and/or for correcting dataresulting from overdetection.

Additional Features

Some embodiments take the form of devices and methods that are directedtoward cardiac activity monitoring. One example may be an implantableloop recorder. Referring to FIG. 1, for monitoring embodiments, ratherthan a therapy decision 22, a decision to store certain data for laterupload may be made instead. For example, some implantable monitors areconfigured to retain data only when a decision is made by the implantthat abnormal and/or potentially malignant activity is occurring. Insome further embodiments, data may be stored when captured data requirescorrection, in order that the sensing and detection characteristics ofthe system and/or implant location may be analyzed to determine itssuitability for long-term use. A monitoring system may also output awarning if a malignant condition is identified, for example byannunciation to the implantee or by communication with an external alertsystem.

The X out of Y counter referred to above may be integrated with apersistence factor as in U.S. Patent Application Publication No.2006-0167503, now U.S. Pat. No. 8,160,697 and titled METHOD FOR ADAPTINGCHARGE INITIATION FOR AN IMPLANTABLE CARDIOVERTER-DEFIBRILLATOR. Apersistence factor requires the X out of Y counter requirement be metfor a predetermined number of consecutive iterations. In theillustrative example, the charge confirmation method of FIG. 22 isintegrated as an additional requirement that follows the persistencefactor. That is, in the illustrative example, charge confirmation wouldbe invoked only after the persistence requirement is satisfied bymeeting the X out of Y counter requirement for a predetermined number ofconsecutive iterations. If/when a non-sustained arrhythmic condition isidentified, the persistence factor and/or X/Y condition may be modifiedas explained in the 2006/0167503 publication.

The above illustrative examples may be embodied in many suitable forms.Some embodiments will be method embodiments incorporating one or more ofthe above features/sub-methods in various combinations. Some embodimentswill be devices adapted to perform one or more of the methods discussedabove and/or a system including implantable devices and associatedexternal programming devices. Some embodiments will take the form oftangible media, such as magnetic, electric, or optical storage media,incorporating controller readable instruction sets. Some embodimentswill take the form of or comprise controllers/microcontrollersassociated with stored instruction sets for directing operations ofvarious components in a device in accordance with one or more methods.

The design details of operational circuitry contained within a canister,such as canister 62 in FIG. 3, may vary widely. Briefly, an illustrativeexample may make use of a microcontroller-driven system which includesan input switch matrix for selecting one or more signal vectors as asensing vector. The switch matrix is coupled to filtering circuitry andat least one input amplifier. The amplified, filtered signal istypically fed to analog-to-digital conversion circuitry. Additionalfiltering of the incoming signal may be performed in the digital domainincluding, for example, 50/60 Hz notch filters. The incoming signal maythen be analyzed using the microcontroller and any associated suitableregisters and logic circuits. Some embodiments include, for example,dedicated hardware for peak or event detection and measurement, or formorpohology analysis such as correlation waveform analysis or wavelettransform analysis.

In several illustrative examples, upon identification of a rhythm thatindicates therapy, a charging operation is undertaken to charge one ormore capacitors to suitable levels for therapy. A charging sub-circuitmay take any suitable form. One example uses a flyback transformercircuit, a structure well known in the art. Any process and/or circuitthat enables relatively low voltage batteries to charge capacitors torelatively high voltages may be used. Some systems also performannunciation and/or communication in response to detected malignancy,for example, to alert the implantee or a medical facility that therapyis imminent or intervention is needed.

The device may further include output circuitry comprising, for example,an output H-bridge or modification thereof for controlling outputpolarity and pulse duration from the high-power capacitor. Controlcircuitry associated with the H-bridge may be included, for example, tomonitor or control current levels for constant current output signals orfor performing diagnostic functions.

The circuitry may be housed in a hermetically sealed canister made ofany suitable material.

The above description details several over-detection identificationmethods and associated data correction methods. Each of these methodsmay be used individually in some embodiments. For example, thewide-complex overdetection identification methods shown below may beused as a stand-alone method for identifying and, if desired, correctingover-detection. In some embodiments, multiple methods are used in asynchronized manner, for example, each of a morphology overdetection,alternating interval, and wide-complex overdetection methods may be usedtogether and may analyze individual detected events or groups ofdetected events continuously. In yet other embodiments, a combination ofthese methods is used in response to given conditions.

In addition to selective activation of the separate over-detectionanalysis methods, there are several ways to integrate the results ofover-detection analysis in addition to those shown by FIGS. 18-21. Thefollowing summaries provide alternatives and variants upon theillustrative examples shown above. In one illustrative example, theoutcomes are integrated as follows:

-   -   1. Waveform Appraisal suspect events can be used in        Overdetection Analysis. Any event marked as an Overdetection by        any method is discarded with associated interval correction,        regardless of any Suspect marker;    -   2. Any event marked Suspect by any method and not marked        Overdetection by any method is Suspect; and    -   3. Any event not marked Overdetection or Suspect is considered        certified once it is no longer eligible to be marked        Overdetection or Suspect.        This example allows detected events that fail waveform appraisal        to be used in later identification of overdetection.

Some examples do not allow detected events that fail waveform appraisalto be used in any subsequent analysis. Thus, in another illustrativeexample, the outcomes are integrated as follows:

-   -   1. Any Waveform Appraisal Marking of a Suspect event prevents        marking of that event by any other method, and that event and        associated intervals are marked WA Suspect;    -   2. Any event marked Overdetection and not WA Suspect is        discarded with associated interval correction, regardless of any        Suspect marker;    -   3. Any event marked Suspect by any method other than Waveform        Appraisal is Suspect unless it was marked Overdetection by any        method; and    -   4. Any event not marked Overdetection or Suspect is considered        certified once it is no longer eligible to be marked        Overdetection or Suspect.

In some embodiments, marking of a detected event as suspect in waveformappraisal disables classification of adjacent events by overdetectionmethods. This prevents a likely noise detection from causing an actualdetection to be discarded. Certain counters may be preserved to avoidimpact by the WA Suspect event as well, for example, when identifying apattern for Alternating Interval Overdetection identification (or toenable the Pattern Found state of the Wide Complex Overdetectionmethods), one may exclude WA suspect events and one or more adjacentevents, if desired.

While voltage and power levels may vary, in one example, an implantablesubcutaneous cardioverter-defibrillator includes charging circuitry andcapacitors sized to receive and hold energy at 1350 volts, and usesoutput circuitry/controller that provide an output that yields adelivered charge of 80 Joules in a biphasic waveform with about 50%tilt. Other voltage, energy and tilt levels (higher and/or lower), andother waveforms may be used, and the load varies in response toelectrode position and physiology. The configuration of output waveformneed not be static, and any suitable methods/configurations forproviding the output may be used (including, without limitation,pre-shock waveforms, monophasic or multiphasic waveforms, adaptation orprogression of therapy energy or voltage level, changes in duration orpolarity, fixed current or fixed voltage, etc.) Some embodiments usetiered therapies including anti-tachycardia-pacing as well ascardioversion and/or defibrillation stimuli. The above generally assumestwo output electrodes (an anode and a cathode), however, it isunderstood that other systems including, for example, arrays, and/orthree or more electrode stimulus systems may be used in which a pair ormore electrodes are used in common.

Analysis may take several forms in terms of the inputs taken. Forexample, a multiple sensing electrode system may be configured to selecta default sensing vector and use the default vector throughout analysis.Other systems may prioritize vectors for use in tiered analysis in whichone vector is analyzed after another. Yet other systems may analyzemultiple vectors simultaneously.

For purposes of conversion into the digital domain, any suitablesampling frequency may be used. Some examples use 256 Hertz; otherfrequencies may be used as desired. Further, the illustrative examplesshown with respect to particular values can be varied, including,without limitation, changes to the refractory periods, event and peakproximity periods, rate ranges, “shockable” event rates, the number ofintervals used to estimate rate, and any other values provided. Analysisusing “suspect” or “certified” events and intervals, waveform appraisal,and other features may vary, and some of these features may be omittedin some embodiments. The completeness of the examples shown is not anindication that all parts are necessary to any given embodiment.

Those skilled in the art will recognize that the present invention maybe manifested in a variety of forms other than the specific embodimentsdescribed and contemplated herein. Accordingly, departures in form anddetail may be made without departing from the scope and spirit of thepresent invention.

What is claimed is:
 1. A method of cardiac signal analysis in animplantable medical device (IMD), the IMD including a number ofimplantable electrodes and operational circuitry coupled to theimplantable electrodes for performing the method of cardiac signalanalysis, the method comprising: a) the operational circuitry detectingevents in a signal; b) the operational circuitry determining whichdetected events to certify for use in rhythm identification by: i)applying interval analysis on a set of detected events in order toidentify overdetected events such that, if a likely overdetection isidentified, the likely overdetection is not certified; ii) applyinganalysis to identify overdetected events using interval and shapecharacteristics of pairs of consecutive events such that, if a likelyoverdetection is identified, the likely overdetection is not certified;and iii) certifying at least some detected events of the set of detectedevents; and c) determining whether a malignant cardiac rhythm isoccurring using certified detected events.
 2. The method of claim 1further comprising the operational circuitry performing waveformappraisal on detected events and, for each event, either passing thedetected event or marking the detected event as suspect, wherein step b)is performed only on detected events that pass waveform appraisal;wherein waveform appraisal operates to determine whether a detectedevent is noisy and, if so, to prevent any noisy detected events frompassing further into the analysis.
 3. The method of claim 1 wherein stepc) comprises the following: determining that an X/Y condition has beenmet, wherein X represents how many malignant events have been identifiedin a set of Y certified detected events; and determining that the X/Ycondition meets a persistence condition.
 4. The method of claim 1wherein the signal in which events are detected is created by: x)capturing a subject signal from electrodes configured to be implanted ina patient subject; and y) conditioning the signal for use in cardiacsignal analysis; wherein steps x) and y) are performed on a continuousbasis, while step a) is performed iteratively using the conditionedsignal.
 5. The method of claim 1 wherein step b)iii) is performed bycertifying all events that pass steps i) and ii) without beingidentified as overdetections.
 6. An implantable cardiac stimulus device(ICSD) comprising electrodes for capturing data from a recipient of theICSD and operational circuitry coupled to the electrodes to analyzesignal captured from the patient, wherein the operational circuitry isconfigured to perform a method comprising the following steps: a)detecting events in a signal; b) determining which detected events tocertify for use in rhythm identification by: i) applying intervalanalysis on a set of detected events in order to identify overdetectedevents such that, if a likely overdetection is identified, the likelyoverdetection is not certified; and ii) applying analysis to identifyoverdetected events using interval and shape characteristics of pairs ofconsecutive events such that, if a likely overdetection is identified,the likely overdetection is not certified; c) determining whether amalignant cardiac rhythm is occurring using certified detected events;and d) delivering therapy in response to the malignant rhythm.
 7. TheICSD of claim 6 wherein the operational circuitry is further configuredsuch that the method further comprises performing waveform appraisal ondetected events and, for each event, either passing the detected eventor marking the detected event as suspect, wherein step b) is performedonly on detected events that pass waveform appraisal; wherein waveformappraisal operates to determine whether a detected event is noisy and,if so, to prevent any noisy detected events from passing further intothe analysis.
 8. The ICSD of claim 6 wherein the operational circuitryis further configured such that step c) comprises the following:determining that an X/Y condition has been met, wherein X represents howmany malignant events have been identified in a set of Y certifieddetected events; and determining that the X/Y condition meets apersistence condition.
 9. The ICSD of claim 6 wherein the operationalcircuitry is further configured such that the signal in which events aredetected is created by: x) capturing a subject signal from electrodesconfigured to be implanted in a patient subject; and y) conditioning thesignal for use in cardiac signal analysis; wherein steps x) and y) areperformed on a continuous basis, while step a) is performed iterativelyusing the conditioned signal.
 10. The ICSD of claim 6 wherein theoperational circuitry is further configured such that step b)iii) isperformed by certifying all events that pass steps i) and ii) withoutbeing identified as overdetections.